Methods and systems for determining daily weighting factors for use in forecasting daily product sales

ABSTRACT

A method for determining daily weight values and store closure coefficients for use in forecasting daily sales patterns for retail products. The method uses historical daily demand data for a product to calculate a daily weight value for the product for each day of the week, each daily weight value representing the ratio of the historical daily demand for a corresponding day of the week to a total of the historical daily demands for the entire week. A daily demand forecast for each day of a forthcoming week is determined by applying the daily weight values to a predetermined weekly demand forecast for the forthcoming week. Historical demand data for weeks including holidays or store closures is used to calculate store closure coefficients, representing the ratio of the historical daily demand for days immediately preceding and following a store closure, to the historical demand for a corresponding day during a regular, non-holiday, week. The store closure coefficients are applied to the daily demand forecasts for days immediately preceding and following store closures or holidays to adjust the daily forecasts to accommodate changes in customer buying patterns resulting from the store closures.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is related to the following co-pending andcommonly-assigned patent application, which is incorporated by referenceherein:

Application Ser. No. 10/703,011 now pending, entitled “METHODS ANDSYSTEMS FOR FORECASTING DAILY PRODUCT SALES,” by Mardie Noble, EjazHaider and Shireengul Islam; filed on the same day herewith.

FIELD OF THE INVENTION

The present invention relates to methods and systems for forecastingproduct demand for retail operations, and in particular to thedetermination of daily sales patterns for products and the use of dailysales patterns in forecasting product sales and implementing productpromotions.

BACKGROUND OF THE INVENTION

Accurately determining demand forecasts for products are paramountconcerns for retail organizations. Demand forecasts are used forinventory control, purchase planning, work force planning, and otherplanning needs of organizations. Inaccurate demand forecasts can resultin shortages of inventory that are needed to meet current demand, whichcan result in lost sales and revenues for the organizations. Conversely,inventory that exceeds a current demand can adversely impact the profitsof an organization. Excessive inventory of perishable goods may lead toa loss for those goods.

Inferior forecasting science and gut feel decisions on inventory havecreated significant stock-out conditions across the industry. Recentstudies quantify stock-outs in the retail industry at 5 to 8%, whileoverstock conditions caused by poor forecasts and buys continue toclimb.

This challenge makes accurate consumer demand forecasting and automatedreplenishment techniques more necessary than ever. A highly accurateforecast not only removes the guess work for the real potential of bothproducts and stores/distribution centers, but delivers improved customersatisfaction, increased sales, improved inventory turns and significantreturn on investment.

Teradata, a division of NCR Corporation, has developed a suite ofanalytical applications for the retail business, referred to as TeradataDemand Chain Management, that provides retailers with the tools theyneed for product demand forecasting, planning and replenishment. Asillustrated in FIG. 1, the Teradata Demand Chain Management analyticalapplication suite 101 is shown to be part of a data warehouse solutionfor the retail industries built upon NCR Corporation's Teradata DataWarehouse 103, using a Teradata Retail Logical Data Model (RLDM) 105.The key modules contained within the Teradata Demand Chain Managementapplication suite 103, organized into forecasting and planningapplications 107 and replenishment applications 109, are:

Demand Forecasting: The Demand Forecasting module 111 provides store/SKU(Stock Keeping Unit) level forecasting that responds to unique localcustomer demand. It continually compares historical and current demandand utilizes several methods to determine the best product demandforecast.

Seasonal Profile The Seasonal Profile module 113 automaticallycalculates seasonal selling patterns at all levels of merchandise andlocation, using detailed historical sales data.

Contribution: Contribution module 117 provides an automaticcategorization of SKUs, merchandise categories and locations bycontribution codes. These codes are used by the replenishment system toensure the service levels, replenishment rules and space allocation areconstantly favoring those items preferred by the customer.

Promotions Management: The Promotions Management module 119automatically calculates the precise additional stock needed to meetdemand resulting from promotional activity.

Automated Replenishment: Automated Replenishment module 121 providessuggested order quantities based on business policies, service levels,forecast error, risk stock, review times, and lead times.

Allocation: The Allocation module 123 determines distribution ofproducts from the warehouse to the store.

The Teradata Demand Chain Management solution described above provides aretailer with improved customer service levels and reductions inunproductive inventory, and is particularly adept at assisting aretailer forecast and plan for seasonal sales cycles. However, for manyretailers the sales pattern of different products varies from day today. Some products sell the same throughout a week while the sale ofsome products follows a certain pattern that, for example, might havehigher sales over the weekend as compared to during the weekend.Holidays also affect the sales pattern for certain products. Before along holiday, sales may be higher for some products, e.g., perishablegoods, milk, soft drinks and other highly consumable items, becausestores may be closed or shopping inconvenient for consumers. Mostretailers and particularly Food and Grocery retailers need to accuratelyforecast daily sales in order to minimize store inventories and optimizestore replenishment schedules.

Therefore, there exists a need for improved demand chain forecastingtools that provide retailers with an accurate picture of product salespatterns over a week. Additionally, such forecasting tools may assist aretailer in forecasting product sales during holiday periods or storeclosures, and in implementing sales for promotions that are shorter thana week in duration.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide a new and usefulmethod for determining daily weight values for use in forecasting dailyproduct sales for a retailer.

It is a further object of the present invention to provide such a methodfor determining store closure coefficients for use in forecastingproduct sales during holiday periods or weeks including store closures.

The method for determining daily weights for use in forecasting dailyproduct sales includes the steps of acquiring historical daily demanddata for a product for a period of at least one week including sevenconsecutive regular business days; and for each weekday, calculating adaily weight value for the product by dividing the historical dailydemand for a corresponding day of the week by a total of the historicaldaily demands for all seven days of the week.

The method for determining store closure coefficients for use inforecasting product sales during holiday periods or weeks includingstore closures includes the steps of acquiring historical daily demanddata for a product for a week including a store closure day; for atleast one store closure impacted day of the week, calculating a newdaily weight value for the product by dividing the historical dailydemand for the store closure impacted day of the week by a total of thehistorical daily demands for all seven days of the week; calculating astore closure coefficient for the product for the store closure impacteddays of the week by determining the difference between the new dailyweight value for the store closure impacted day and a prior calculateddaily weight value for a corresponding day during a week not including astore closure, and dividing this difference by the new daily weightvalue for the store closure impacted day.

Still other aspects of the present invention will become apparent tothose skilled in the art from the following description of variousembodiments. As will be realized the invention is capable of otherembodiments, all without departing from the present invention.Accordingly, the drawings and descriptions are illustrative in natureand not intended to be restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 provides an illustration of a forecasting, planning andreplenishment software application suite for the retail industries builtupon NCR Corporation's Teradata Data Warehouse.

FIG. 2 provides an illustration of a regular store week for calculationof daily weights to be used in daily sales forecasting.

FIG. 3 is a table listing an exemplary set of default uplift valuesutilized to adjust daily weights during a store closure week.

FIGS. 4 through 7 illustrate a process for calculating store closurecoefficients for modifying daily sales forecasts during weeks includingholidays or other store closures.

FIG. 8 is a flow diagram illustrating a process for determining dailyforecasts for regular and store closure weeks.

FIGS. 9A and 9B, taken together, provide a data flow diagram for asystem for calculating daily weights and forecasts.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, reference is made to the accompanyingdrawings that form a part hereof, and in which is shown by way ofillustration specific embodiments in which the invention may bepracticed. These embodiments are described in sufficient detail toenable one of ordinary skill in the art to practice the invention, andit is to be understood that other embodiments may be utilized and thatstructural, logical, optical, and electrical changes may be made withoutdeparting from the scope of the present invention. The followingdescription is, therefore, not to be taken in a limited sense, and thescope of the present invention is defined by the appended claims.

The Teradata Demand Chain Management solution described above andillustrated in FIG. 1 provides a retailer with improved customer servicelevels and reductions in unproductive inventory, and is particularlyadept at assisting a retailer forecast and plan for seasonal salescycles. The improved demand chain forecasting tool described hereinprovides retailers with an accurate picture of product sales patternsover a week, determines the effect of holidays and store closures onthese weekly product sales patterns, and integrates these results withseasonal sales patterns and historical demand to accurately forecastdaily sales.

The sales patterns of different products vary from day to day. Someproducts sell the same throughout a week whereas other products follow acertain pattern and might have higher sales over the weekend as comparedto weekday sales. Holidays and store closures both affect weekly salespattern. A holiday may affect regular weekly sales patterns depending onthe day of the week the holiday will be since consumers may decide toanticipate purchases or may need to purchase certain items to realizetheir holiday plans. In addition, store closures, whether on a holidayor not will also affect weekly patterns since some of the sales thatwould have been realized on that day will be occur in the few previousor following days.

The weekday sales forecasting tool described below utilizes dailyweights calculated from historical weekly sales data for normal weekswherein regular operating hours are maintained, and sales data fromweeks including holiday or other store closures, to forecast futureweekly sales and implement product promotions.

Determination of Daily Weights Regular Week Calculations

Daily weights, or the percentage each day of the week contributes to theweekly sales for a product, calculated for each day of the week are usedin forecast calculations for non-promotional and promotional sales thatare shorter than a week and in any other forecast calculation where theforecast is established for less than one week.

A Regular Week, defined as a week without holidays or store closures, isillustrated in the table of FIG. 2. Three weeks, labeled Week 1, Week 2and Week 3, are illustrated in FIG. 2. Within each week, the letter “R”is used to represents a regular business day. Week 2, including sevenregular business days each represented with an “R”, preceded by tworegular business days at the end of Week 1, and followed by two regularbusiness days at the beginning of Week 3, is considered a Regular Week.The seven days comprising Week 2, may be Sunday through Saturday, Mondaythrough Sunday, or any seven consecutive days of interest to a retailer.

In the implementation described herein, the data for a week can only beused for daily weight calculation if that week has no store closures andthere are no store closures in the two days before the beginning and twodays after the end of the week in consideration.

Daily weights are determined for a product through employment of thefollowing formula:

$W_{{day}\; 1} = \frac{{Dmnd}_{{day}\; 1}*100}{\sum\limits_{n = 1}^{7}\;{Dmnd}_{dayn}}$

wherein W_(day1) is the weight calculated for day 1 of a regular week,Dmnd_(day1) is the demand for the product on day 1, and Dmnd_(dayn) isthe demand for the product on day n. Weights are calculated for each ofthe seven days of the regular week, day 1 through day 7.

Previous daily weights and current week daily weights are combined usinga global system defined adaptive factor to determine the new dailyweight for daily forecast calculation. The global adaptive factorrepresents the percentage of previous daily weights and current dailyweights to be used for new daily weight calculations. Previous dailyweights and current week daily weights are combined in accordance withthe following equation:W=(1−A)*W _(n−1) +A*W _(n)

Wherein W_(n-1) is the previously calculated daily weight, W_(n) is thecurrently calculated daily weight from last week's demand, A is theadaptive factor, and W is the new daily weight. A suggested value foradaptive factor A is 20%, thus the new daily weight is determined bycombining 80% of the previous daily weight with 20% of the current weekdaily weight.

Determination of Daily Weights Weekly Pattern Calculations with StoreClosures

A Store Closure Week is defined as a week that includes a store closureduring that week or in the period two days before the beginning or oneday after the end of the week in consideration.

In the implementation described herein, the data for a week can only beused for daily weight calculation if that week has no store closures andthere are no store closures in the two days before the beginning and twodays after the end of the week in consideration. Store closurecoefficients are calculated and applied to the two days prior to, andone day following a store closure as shown in FIGS. 4 through 7 anddescribed below. In the absence of calculated store closurecoefficients, default uplifts may be applied to estimate demand for thedays preceding and following a store closure. The table of FIG. 3 showsone possible set of default uplift values of 20% the day two days beforea store closure, 50% for the day immediately prior to the store closure,and 30% for the day following the store closure. Of course, a retailermay elect different values for default uplifts that they believe aremore appropriate for their business.

One process for calculating store closure coefficients for store closureweeks is described below with reference to FIGS. 4 through 7. The firststep in calculating store closure coefficients is to organize the storeclosure week so that the holiday or store closure is placed in themiddle of the week, as illustrated in row 401 of FIG. 4. The holiday orstore closure day is represented by the letter “H”, while the letter “R”indicates a regular business day. The store closure day 403 is shown tobe the fourth day of the store closure week, regardless of the actualday of the week, i.e., Sunday, Monday, etc., the store closure occurs.

Daily weights are calculated for the two days prior and one dayfollowing the store closure using the formula for daily weightcalculation given earlier, as illustrated in row 501 of FIG. 5, whereinDW⁻² represents the daily weight for the day two days prior to the storeclosure, DW⁻¹ represents the day immediately prior to the store closure,DW₀ is the daily weight for the store closure day, and DW₁ is the dailyweight for the day following the store closure. Of course, the dailyweight for a full day closure, DW₀, will be zero.

The difference in daily weights between corresponding days in the storeclosure week and a regular week are determined for the two days priorand one day following the store closure. The difference in daily weightsfor any one of the store closure impacted days is the daily weight forthat day during a store closure week less the daily weight for acorresponding day during a regular week. The difference values, shown inrow 601 of FIG. 6, are represented by the terms Diff₂, Diff⁻¹, Diff₀ andDiff₁. Diff₂, Diff⁻¹, Diff₀ and Diff₁ correspond to the day two daysprior to store closing, the day one day prior to the store closing, theday of the store closure, and the day following the store closure,respectively.

The difference values Diff⁻², Diff⁻¹, Diff₀ and Diff₁ are divided by thedaily weights for the corresponding store closure impacted days, DW⁻²,DW⁻¹, DW₀ and DW₁, as shown in row 701 of FIG. 7. The store closurecoefficients, or holiday coefficients, identified in row 703 as HC⁻²,HC⁻¹ and HC₁, are the values Diff⁻²/DW⁻², Diff⁻¹/DW⁻¹ and Diff₁/DW⁻²,respectively, expressed in percentage.

Store closure week daily weights are calculated for every week thatincludes a store closure. Previous store closure week daily weights andcurrent store closure week daily weights are combined using the sameadaptive factor as used in regular week daily weight blending, discussedabove.

An example calculation for a store closure week with the following dailyweights is provided below. The first row, labeled DW, of the table belowcontains the daily weights determined for the two days prior and one dayfollowing a recent store closure week. These values represent increasedsales for the store closure effected days. The second row, labeled RW,contains the daily weights for a regular week. In this example, thedaily weight for each day of the regular week is 0.17429, or 1/7 ofweekly sales.

TABLE 1 Sample Holiday Coefficient Calculation −2 −1 0 +1 DW 0.1714290.214286 0 0.185714 RW 0.142857 0.142857 0.142857 0.142857 Diff 0.0285710.071429 −0.14286 0.042857 Diff/DW 0.2 0.5 −1 0.3 HC 20% 50% 30%

The holiday coefficients determined for the example regular and storeclosure week daily weight values shown are: 20% for the day two daysprior to the store closure, 50% for the day immediately before the storeclosure, and 30% for the day following the store closure. Thesecoefficients are used in sales forecasting to adjust daily forecasts forstore closure weeks, e.g., sales forecasts for a day following a storeclosure will be increased by 30% over a regular day forecast for thatday.

Daily Forecasts

A process for determining daily forecasts for regular and store closureweeks is illustrated in the flow diagram of FIG. 8. The process requiresstore closure information obtained from a holiday closure calendarcontaining a list of stores and dates specifying when the stores will beclosed, as well as a minimum of two weeks daily demand, saved in datastores 801 and 803, respectively.

Regular week daily weights are determined in step 805. The daily weightsare calculated from daily sales history for non-holiday weeks obtainedfrom data store 803. Weeks with holidays are not used in the regularweek daily weight calculations. If four weeks of daily sales history isavailable, the regular daily weights for any day of the week can becalculated by summing the four weeks of sales data for the weekday, anddividing this sum by the total sales for all days over the four weeks.For example: Sunday weight=Sum(sales on Sundays)/Sum(all sales for 4weeks); and Saturday weight=Sum(sales on Saturdays)/Sum(all sales for 4weeks).

Calculated daily weights are saved, 807, for use in daily forecastcalculations and promotion management. As additional weekly sales datais accumulated, new weights are calculated and blended with the savedweights, the resultant replacing the previously saved values, as shownin step 809.

Similarly, holiday daily weights are determined as shown in step 813.Holiday, or store closure, adjustment factors, or coefficients, arecalculated for pre and post store closure days and saved, 815. In theabsence of store closure data or calculated holiday daily weights,default store closure adjustments 811 are provided during initialprogram load.

Regular week daily weights from data store 807 are combined with storeclosure coefficients from data store 815 in step 817 to generate dailyforecasts 819.

FIGS. 9A and 9B, taken together, provide a data flow diagram for asystem for calculating daily weights. Data concerning product sales,retail store locations, holidays and store closures, daily weights andstore closure adjustment factors is saved within a data store, such asthe Teradata data warehouse, distributed by NCR Corporation of Dayton,Ohio. Historical weekly sales information, store location information,product information and daily product demand data is stored in thedatabase tables labeled YearWeek 901, Location 903, M_Location_Rel 905,Product 907 and Daily_Prod_Dmnd 909. A store holiday closure calendarand fiscal calendar are saved in database tables HolidayClosureCalendar911 and FiscalCalendar 913, respectively.

Prior week daily demand values are saved in a database tableDWDailyC1Dmnd 915, and prior calculated daily weights and holidaycoefficients are saved in a database table DWDailyweights 919. Run-timeoptions, such as adaptive factors, are stored in a database tableParameter 917. Default weight values are saved to database tableDayWeekWeight 921.

Weights can be calculated at a national, regional, district or storelocation level. Store location information, product information andcurrent week daily product demand data is extracted from database tables901, 903, 905, 907 and 909 and accumulated at theNation-Region-District-Location (NRDL) level selected by the retailer asshown in step 923. The user can set any of the following levels for thedaily weight calculation: Nation, Region, District, orStore/Distribution Center/Profile Store Group. The accumulated currentweek demand data is stored in a temporary table DWDailyC1Dmnd_Temp 925.

The holiday closure calendar and fiscal calendar, saved in databasetables 911 and 913, respectively, are consulted to determine whether thecurrent week includes a store closure as shown in step 927. Fornon-holiday weeks, new regular week daily weights are calculated fromthe data stored in temporary table DWDailyC1Dmnd_Temp 925 as shown instep 929. The calculated current week daily weights, run time optiondata from Parameter table 917, prior calculated daily weights fromDWDailyweights table 919, and default weight values from DayWeekWeighttable 921 are combined as shown in step 931. New regular week dailyweights are blended with prior daily weights from table 919 using theresponse factor from table 917. In the absence of prior daily weightvalues, the new regular week daily weights are blended with defaultdaily weights from table 921 using the response factor from table 917.

For holiday or store closure weeks, new holiday coefficients arecalculated as shown in step 929. The calculated current holidaycoefficients, run time option data from Parameter table 917, priorcalculated holiday coefficients from DWDailyweights table 919, anddefault weight values from DayWeekWeight table 921 are combined as shownin step 935. New regular week daily weights are blended with prior dailyweights from table 919 using the response factor from table 917. In theabsence of prior holiday coefficient values, the holiday coefficientsare blended with default values from table 921 using the response factorfrom table 917.

Regular week daily weights are combined with holiday coefficients instep 937 to generate a new table of daily weights, DWDailyweights_new939. This new table is merged with the old table DWDailyweights 919, andtable DWDailyClDmnd 915 is replaced with a new table as shown in step941.

CONCLUSION

The improved demand chain forecasting tool described above useshistorical demand, seasonal patterns, weekly patterns, and the effect ofholidays and store closures to determine at any level of a retail storeor merchandise hierarchy the percentage of weekly sales forecast thatcan be attributed to each day of the week. Through accurate dailyforecasting, a retailer is able to minimize store inventories andoptimize store replenishment schedules. Additionally, such forecastingtool may assist a retailer in implementing sales promotions that areless than a week in duration.

The foregoing description of various embodiments of the invention hasbeen presented for purposes of illustration and description. It is notintended to be exhaustive nor to limit the invention to the precise formdisclosed. Many alternatives, modifications, and variations will beapparent to those skilled in the art in light of the above teaching. Forexample, the improved demand chain forecasting tool described herein isnot limited to any particular retail business, and may be used toforecast daily sales for a particular store, sales region, storedepartment, class of merchandise, or product. A business need notimplement all the features included in the system described herein. Forexample, a convenience store that is open seven days a week throughoutthe year, will not need to calculate store closure coefficients or alterdaily forecasts for weeks including store closures. Accordingly, thisinvention is intended to embrace all alternatives, modifications,equivalents, and variations that fall within the spirit and broad scopeof the attached claims.

1. A method to determine daily weight values, each daily weight valuerepresenting the percentage a day of a week contributes to the weeklysales for said product, said method comprising the steps of: acquiringhistorical daily demand data for said product for a period of at leastone regular week, said regular week including seven consecutive regularbusiness days; for each day of said regular week, calculating a dailyweight value for said product by dividing the historical daily demandfor a corresponding day of said regular week by a total of saidhistorical daily demands for all seven days of said regular week;acquiring historical daily demand data for said product for anadditional regular week, said additional regular week including sevenconsecutive regular business days; for each day of said additionalregular week, calculating a new daily weight value for said product bydividing the historical daily demand for a corresponding day of saidadditional regular week by a total of said historical daily demands forall seven days of said additional regular week; and blending said newdaily weight values with said first daily weight values to determineupdated daily weight values for each weekday in accordance with thefollowing equation: updated daily weight value=(1−A)*prior-calculateddaily weight value+A*new daily weight value, wherein A comprises apredetermined adaptive factor that establishes the proportion of saidnew daily weight values and said prior-calculated daily weight valuesthat are blended.
 2. The method to determine daily weight values for usein forecasting daily demand for a product in accordance with claim 1,wherein: said regular week is immediately preceded by two regularbusiness days and immediately followed by two regular business days. 3.A method to determine daily weight values, each daily weight valuerepresenting the percentage a day of a week contributes to the weeklysales for said product, said method comprising the steps of: acquiringhistorical daily demand data for said product for a period of at leastone regular week, said regular week including seven consecutive regularbusiness days; for each day of said regular week, calculating a dailyweight value for said product by dividing the historical daily demandfor a corresponding day of said regular week by a total of saidhistorical daily demands for all seven days of said regular week;acquiring historical daily demand data for said product for anadditional week, said additional week including a store closure day; forat least one store closure impacted day of said additional week,calculating a new daily weight value for said product by dividing thehistorical daily demand for said store closure impacted day of saidadditional week by a total of said historical daily demands for allseven days of said additional week; calculating a store closurecoefficient for said product for said at least one store closureimpacted day of said additional week by determining the differencebetween said new daily weight value for said at least one store closureimpacted day and the prior-calculated daily weight value for acorresponding day during said regular week, and dividing said differenceby said new daily weight value for said at least one store closureimpacted day.
 4. The method to determine daily weight values for use inforecasting daily demand for a product in accordance with claim 3,wherein: said store closure impacted days comprise the two weekdaysprior to said store closure day and the one weekday following said storeclosure day.
 5. In a system that determines a daily demand forecast fora product for each day of a forthcoming week from a daily weight valuefor each weekday, a method for determining said daily weight values,said method comprising the steps of: acquiring historical daily demanddata for said product for a period of at least one regular week, saidregular week including seven consecutive regular business days; for eachweekday, calculating a daily weight value for said product by dividingthe historical daily demand for a corresponding day of said regular weekby a total of said historical daily demands for all seven days of saidregular week; acquiring historical daily demand data for said productfor an additional regular week, said additional regular week includingseven consecutive regular business days; for each day of said additionalregular week, calculating a new daily weight value for said product bydividing the historical daily demand for a corresponding day of saidadditional regular week by a total of said historical daily demands forall seven days of said additional regular week; and blending said newdaily weight values with said first daily weight values to determineupdated daily weight values for each weekday in accordance with thefollowing equation: updated daily weight value=(1−A)*prior-calculateddaily weight value+A*new daily weight value, wherein A comprises apredetermined adaptive factor that establishes the proportion of saidnew daily weight values and said prior-calculated daily weight valuesthat are blended.
 6. In a system that determines a daily demand forecastfor a product for each day of a forthcoming week including a storeclosure day through application of store closure coefficients toprior-calculated daily demand forecasts for a week not including a storeclosure, a method for determining said store closure coefficients, saidmethod comprising the steps of: acquiring historical daily demand datafor said product for a week including a store closure day; for at leastone store closure impacted day of said week, calculating a new dailyweight value for said product by dividing the historical daily demandfor said store closure impacted day of said additional week by a totalof said historical daily demands for all seven days of said additionalweek; calculating a store closure coefficient for said product for saidat least one store closure impacted day of said week by determining thedifference between said new daily weight value for said at least onestore closure impacted day and the prior calculated daily weight valuefor a corresponding day during said week not including a store closure,and dividing said difference by said new daily weight value for said atleast one store closure impacted day.
 7. The method to determine storeclosure coefficients for use in forecasting daily demand for a productin accordance with claim 6, wherein: said store closure impacted dayscomprise the two weekdays prior to said store closure day and the oneweekday following said store closure day.